Neuroprosthetic devices have been developed to alleviate deficits in motor, sensory and cognitive functions. Cochlear implants for example, one of the inaugural neuroprosthetics, are now widely used among those hard of hearing. In recent years, neuroprosthetic devices targeted towards movement restoration have seen considerable advances. For example, one approach aimed at restoring locomotion in patients suffering from spinal cord injury (SCI) involves electrically simulating the spinal cord during voluntary and/or assisted stepping. The approach typically involves delivering epidural electrical spinal cord stimulation (EES) based on brain activity monitored by a neurosensor, such as an electrode array. In this way, application of the electrical stimulation may be closed-loop controlled responsive to changes in brain activity.
The effectiveness of neuroprosthetic stimulation in restoring motor movement functionality may depend on its delivery timing (e.g., when it is delivered during execution of the motor movement). In the absence of a spinal cord injury, brain generated movement commands may be transmitted to appropriate muscles tasked with carrying out the desired movement. However, due to the spinal cord injury, transmission of such motor movement commands may be interrupted. Thus, the calibration of the neuroprosthetic may involve learning the unique brain activity patterns associated with the motor movement command of interest to be able to detect when the motor movement command is being generated. Delivery of the stimulation can then be coordinated with the brain commanded motor movement to mimic how the motor movement command would ordinarily be communicated to the muscles by the spinal cord absent injury. Calibrating the neuroprosthetic therefore, typically involves determining when to provide the stimulation to achieve optimal motor recovery.
Thus, during calibration, brain activity may be monitored while the motor movement (e.g., stepping) is repeatedly attempted. For example, motor cortex activity may be monitored following a prompt to perform the motor movement. In one example, the prompt may include operating an external device (e.g., harness and treadmill in the example of stepping) to cue and/or facilitate a motor task. However, it should be appreciated that the movement does not need to be executed in order to monitor brain activity. That is, brain activity monitored during an attempted movement may be similar to activity observed during volitionally executed movements. Thus, execution of the motor movement is not required during calibration of the neuroprosthetic system. Furthermore, stimulation may not be triggered by brain activity associated with non-volitional movements (e.g., passive or simulated movements). The recorded brain activity is then time-aligned with the prompts to attempt to perform motor movements in order to learn the neural activity patterns associated with the appropriate delivery timing of the stimulation. After calibration, electrical stimulation can be triggered in a closed-loop manner by comparing current brain activity to the brain activity patterns associated with the desired delivery timing of the stimulation. In this way, temporal acuity of stimulation may be improved.
However, the inventors herein have recognized potential issues with such systems. As one example, stimulating the spinal cord may affect neural activity in the brain. Thus, the neural activity recorded during and/or after stimulating the spinal cord may be different than it would otherwise be in the absence of the electrical stimulation. Such changes in neural activity, when not accounted for in the calibration of the neuroprosthetic device, can lead to aberrant stimulation. That is, due to the effects of the electrical stimulation on neural activity, stimulation of the spinal cord may be triggered when it is not desired, and/or not triggered when desired. When the stimulation is mis-applied, the effectiveness of the neuroprosthetic in restoring motor movement may be reduced. For example, the neural activity resulting from electrical stimulation of the spinal cord may closely resemble neural activity associated with a desired time to deliver the electrical stimulation. As such, the neural activity resulting from electrical stimulation of the spinal cord can be misidentified during closed loop control as a desired time to stimulate, leading to over-stimulation of the spinal cord. Thus, closed-loop control schemes may improperly identify neural activity resulting from the electrical stimulation as a desired time to stimulate the spinal cord. Such errors in the delivery timing of the electrical stimulation may prohibit and/or reduce the restoration of motor movements.
In one example, at least some of the issues described above may be at least partially addressed by a method for, during a first mode, monitoring motor cortex activity while not stimulating any nerve fibers, and during a second mode, stimulating the one or more nerve fibers, and monitoring motor cortex activity during and after stimulating the one or more nerve fibers. The method may further comprise generating a model that predicts motor movement commands based on the motor cortex activity monitored during both the first and second modes. In this way, the way in which the motor cortex responds to electrical stimulation may be learned and accounted for during closed-loop control of spinal cord stimulation. As such, undesirable stimulation events that would be triggered when not accounting for the effects of stimulation on neural activity, may be reduced. Furthermore, the number of stimulation events that would not have been triggered at the appropriate times when not accounting for the effects of stimulation on neural activity, may be reduced as well.
In some examples, the method may comprise executing the first mode before the second mode. After executing the first mode, an initial motor cortex activity profile may be generated based on the motor cortex activity monitored during the first mode. Then, a new motor cortex activity profile may be generated based on the motor cortex activity monitored both during the first and the second mode. In particular, differences in neural activity between the first and second modes may be the result of the electrical stimulation provided in the second mode. Thus, the new motor cortex activity profile is calibrated to interpret neural activity from both modes, in the presence and absence of stimulation, at the times that would be appropriate to trigger the stimulation as being the correct times appropriate to trigger the stimulation.
Thus, the method may comprise learning a neural response signal that results from stimulating the spinal cord. By modifying the motor cortex activity profile based on the neural response signal, the accuracy of predictions of future motor cortex activity patterns may be increased. In particular, when electrical stimulation is applied to the spinal cord, neural activity resulting from the electrical stimulation may be more accurately anticipated and accounted for in the motor cortex activity profile. By accounting for such changes in motor cortex activity resulting from electrical stimulation of the spinal cord, delivery of the electrical stimulation may be more effectively timed to restore motor movement.
As another example, a method may comprise, while monitoring motor cortex activity during attempted execution of a desired motor movement, electrically stimulating a nerve fiber at a desired instance following a motor movement command. The method may additionally comprise, while inducing execution of the desired motor movement, electrically stimulating the nerve fiber under closed-loop feedback control based on current motor cortex activity and the monitored motor cortex activity. The motor movement command may be generated by a motor cortex and may command for execution of the desired motor movement.
In yet another example, a neuroprosthetic system may comprise a neurosensor for monitoring neural activity, an electrical stimulator for delivering electrical stimulation to one or more nerve fibers, a controller in communication with the neurosensor and electrical stimulator including computer readable instruction stored in non-transitory memory for triggering electrical stimulation events based on neural activity recordings received from the neurosensor, generating a set of computer readable instructions using neural activity profile based on neural activity monitored during one or more repetitions of a motor event stored in the non-transitory memory, electrically stimulating a nerve fiber during one or more repetitions of that motor event, generating another set of computer readable instructions using neural activity profile based on neural activity monitored during the one or more repetitions in the absence of stimulation and one of more repetitions in the presence of stimulation, and replacing the prior set of instructions stored in the non-transitory memory with the new set of instructions.
In this way, by accounting for how the brain responds to electrical stimulation of the spinal cord, undesirable stimulations of the spinal cord which may impede restoration of motor movement may be reduced. Thus, neural responses resulting from electrical stimulation of the spinal cord that occur during the times when the stimulation is not desired are learned to not be associated with a desired time to stimulate the spinal cord, and unintended spinal cord stimulations may be avoided. Furthermore, neural responses observed in the presence of electrical stimulation of the spinal cord during the times that are appropriate to trigger the stimulation are learned to be associated with a desired time to stimulate the spinal cord, and not triggering spinal cord stimulations at the appropriate time may also be avoided. Said another way, spinal cord simulations may be more accurately timed to coincide with brain generated motor movement commands to more optimally promote execution of the motor movement.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.